Designing Eco-Friendly Kambuik Shopping Bags: A Quality Function Deployment Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of plastic shopping bags has raised concerns regarding environmental sustainability.To address this issue, technological advancements have enabled the development of eco-friendly shopping bags.These bags can be returned, contributing to a more eco-conscious shopping experience.However, aligning these bags with customer preferences and needs presents a significant challenge.To overcome this, we propose the use of the Quality Function Deployment (QFD) method.Applying the QFD method, we have identified the technical characteristics of eco-friendly shopping bags, utilizing woven Mansiang leaf material.The resulting bags measure 55 cm in length, 35 cm in height, and 8 cm in thickness.Furthermore, we present innovative designs, including bags, wallets, and flower holders.The inherent strength of the woven Kambuik material allows for efficient carrying capacity, offering a viable alternative to plastic shopping bags.Notably, Kambuik is derived from plants and has minimal environmental impact.Our research confirms the feasibility of designing safe and ecofriendly Mansiang leaf shopping bags, aligning with sustainable practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it